Bioimpedance-based identification of malnutrition using fuzzy logic

Abstract

Protein-energy malnutrition reduces the quality of life, lengthens the time in hospital and dramatically increases mortality. Currently there is no simple and objective method available for assessing nutritional status and identifying malnutrition. The aim of this work is to develop a novel assistance system that supports the physician in the assessment of the nutritional status. Therefore, three subject groups were investigated: the first group consisted of 688 healthy subjects. Two additional groups consisted of 707 patients: 94 patients with primary diseases that are known to cause malnutrition, and 613 patients from a hospital admission screening. In all subjects bioimpedance spectroscopy measurements were performed, and the body composition was calculated. Additionally, in all patients the nutritional status was assessed by the subjective global assessment score. These data are used for the development and validation of the assistance system. The basic idea of the system is that nutritional status is reflected by body composition. Hence, features of the nutritional status, based on the body composition, are determined and compared with reference ranges, derived from healthy subjects' data. The differences are evaluated by a fuzzy logic system or a decision tree in order to identify malnourished patients. The novel assistance system allows the identification of malnourished patients, and it can be applied for screening and monitoring of the nutritional status of hospital patients.

Insulin Resistance in Nondiabetic Peritoneal Dialysis Patients

“…The values for LTI were compared with an age-and gender-matched reference population and, according to this, patients with LTI ,10th percentile were considered to have protein wasting (22). Patients that simultaneously presented with rel.FM,10% and LTI ,reference range were characterized as having protein-energy wasting (23).…”

Evaluating Cardiovascular Risk in Chronic Kidney Disease Patients: A Biomarker Approach

“…Controlling blood pressure especially systolic blood pressure can be difficult in CKD patients (Coresh et al 2001;Ravera et al 2006;Tonelli et al 2001). Most CKD patients are volume overload (Hung et al 2014;Wieskotten et al 2008) and it is a risk factor for CVD.…”

Fluid Overload, Pulse Wave Velocity, and Ratio of Brachial Pre-Ejection Period to Ejection Time in Diabetic and Non-Diabetic Chronic Kidney Disease

“…The relative hydration status (△HS = fluid overload/ECW) has been used as an indicator of fluid status in a previous study [5]. In the present study, “Fluid overload” was defined as ΔHS of 7% or more corresponding to the value of 90 th percentile for the normal reference population when the fluid status was measured with the same technology [3], [22], [23].…”

“…Fluid overload can be calculated from the difference between the normal expected and measured extracellular water (ECW) [21]. Fluid overload value, ECW, intracellular water (ICW) and total body water (TBW) were determined from the measured impedance data based on the model of Moissl et al [22], [23]. The relative hydration status (△HS = fluid overload/ECW) has been used as an indicator of fluid status in a previous study [5].…”